Expected Conditional Characteristic Function-based Measures for Testing Independence

DOI10.1080/01621459.2019.1604364zbMath1445.62091OpenAlexW2938204025MaRDI QIDQ5130638

Chenlu Ke, Xiangrong Yin

Publication date: 28 October 2020

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2019.1604364

zbMATH Keywords

independencecharacteristic functionreproducing kernel Hilbert spacecategorical variables

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (12)

A brief review of linear sufficient dimension reduction through optimization ⋮ Partition-based feature screening for categorical data via RKHS embeddings ⋮ Measuring association with Wasserstein distances ⋮ The Binary Expansion Randomized Ensemble Test ⋮ Partial sufficient variable screening with categorical controls ⋮ Nonlinear directed acyclic graph estimation based on the kernel partial correlation coefficient ⋮ Generalized martingale difference divergence: detecting conditional mean independence with applications in variable screening ⋮ Projection divergence in the reproducing kernel Hilbert space: asymptotic normality, block-wise and slicing estimation, and computational efficiency ⋮ Sufficient variable screening with high-dimensional controls ⋮ Multivariate tests of independence based on a new class of measures of independence in reproducing kernel Hilbert space ⋮ Fourier-type tests of mutual independence between functional time series ⋮ High-dimensional variable screening through kernel-based conditional mean dependence

Uses Software

• bootlib

Cites Work

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• Algorithmic Learning Theory

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